AIMC Topic: Machine Learning

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A multiple hold-out framework for Sparse Partial Least Squares.

Journal of neuroscience methods
BACKGROUND: Supervised classification machine learning algorithms may have limitations when studying brain diseases with heterogeneous populations, as the labels might be unreliable. More exploratory approaches, such as Sparse Partial Least Squares (...

Machine learning based methodology to identify cell shape phenotypes associated with microenvironmental cues.

Biomaterials
Cell morphology has been identified as a potential indicator of stem cell response to biomaterials. However, determination of cell shape phenotype in biomaterials is complicated by heterogeneous cell populations, microenvironment heterogeneity, and m...

Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods.

Artificial intelligence in medicine
OBJECTIVE: The combination of phenomic data from electronic health records (EHR) and clinical data repositories with dense biological data has enabled genomic and pharmacogenomic discovery, a first step toward precision medicine. Computational method...

Classification of patients with MCI and AD from healthy controls using directed graph measures of resting-state fMRI.

Behavioural brain research
Brain network alterations in patients with Alzheimer's disease (AD) has been the subject of much investigation, but the biological mechanisms underlying these alterations remain poorly understood. Here, we aim to identify the changes in brain network...

Machine learning approaches in medical image analysis: From detection to diagnosis.

Medical image analysis
Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging...

Extreme learning machine and adaptive sparse representation for image classification.

Neural networks : the official journal of the International Neural Network Society
Recent research has shown the speed advantage of extreme learning machine (ELM) and the accuracy advantage of sparse representation classification (SRC) in the area of image classification. Those two methods, however, have their respective drawbacks,...

Machine learning approaches in MALDI-MSI: clinical applications.

Expert review of proteomics
INTRODUCTION: Despite the unquestionable advantages of Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging in visualizing the spatial distribution and the relative abundance of biomolecules directly on-tissue, the yielded data is co...

Machine learning for medical images analysis.

Medical image analysis
This article discusses the application of machine learning for the analysis of medical images. Specifically: (i) We show how a special type of learning models can be thought of as automatically optimized, hierarchically-structured, rule-based algorit...

Investigation of the influence of protein corona composition on gold nanoparticle bioactivity using machine learning approaches.

SAR and QSAR in environmental research
The understanding of the mechanisms and interactions that occur when nanomaterials enter biological systems is important to improve their future use. The adsorption of proteins from biological fluids in a physiological environment to form a corona on...

Neural network training as a dissipative process.

Neural networks : the official journal of the International Neural Network Society
This paper analyzes the practical issues and reports some results on a theory in which learning is modeled as a continuous temporal process driven by laws describing the interactions of intelligent agents with their own environment. The classic regul...